Nishikawa plots

First, we plot the presence/absence data from the Nishikawa dataset for sauries. Generally, positive sampling points are pretty sparse.

January-March

April-June

July-September

October-December

Base model (Model 1)

Then, we build the basic sauries model with all the predictors included. Note that the environmental predictors are mean values over 1956-1981.

  1. longitude
  2. latitude
  3. season
  4. temperature (\(^{\circ} C\))
  5. oxygen (\(mol \; m^{-3}\))
  6. pH
  7. chlorophyll (\(mol \; m^{-3}\))
  8. salinity (ppt)
  9. mixed layer thickness (m)
  10. nitrate (\(mol \; m^{-3}\))
  11. phosphate (\(mol \; m^{-3}\))
  12. ammonium (\(mol \; m^{-3}\))
  13. broad-scale thermal gradient (\(\Delta ^{\circ} C \; km^{-1}\))
  14. broad-scale salinity gradient (\(ppt \; km^{-1}\))
  15. eddy kinetic energy (\(m^{-2} \; s^{-2}\))
  16. depth (m)
  17. distance from nearest coast (km)
  18. mean probability of adult occurrence for three sauries species (Cololabis saira, Cololabis adocetus, and Scomberesox saurus)
## [1] "training AUC: 0.9721"
## [1] "testing AUC: 0.9729"

Then, we extrapolate for the rest of \(40^{\circ}N\)-\(40^{\circ}S\) and present seasonal distribution maps. The distribution maps are shown side-by-side with the Nishikawa maps.

Generally speaking, even with the \(0-360^{\circ}\) longitude, there are still visible artefacts for the sauries’ model.

January-March

April-June

July-September

October-December

Model without geographical location (Model 2)

## [1] "training AUC: 0.9755"
## [1] "testing AUC: 0.9673"

Again, each seasonal distribution map is shown side-by-side with its corresponding Nishikawa seasonal chart.

The artefacts are gone when using Model 2.

January-March

April-June

July-September

October-December

Increasing confidence of distribution maps.

For this section, we use Model 1 (full model). We use the same \(10 \times 10\) grid.

January-March

Similar to the yellowfin tuna, for each season, we associate the \(10 \times 10\) grid with the \(1 \times 1\) grid cells. Then, we limit the area to \(10 \times 10\) grid cells with sampling points.

We only leave \(10 \times 10\) grid cells that have sampling points within a certain % area threshold. We first do this for a more conservative 25% threshold.

We also do it for a more liberal 10% threshold.

Then, we replicate this across the 3 other seasons…

April-June

July-September

October-December